Does traditional wildlife disease data allow for spatial analysis in Ontario, Canada: limits and opportunities

Spatial data related to wildlife and wildlife diseases is often limited, sparse, or designed for one-time use which can lead to difficulties understanding the distribution and spatial characteristics of health outcomes and... [ view full abstract ]

Spatial data related to wildlife and wildlife diseases is often limited, sparse, or designed for one-time use which can lead to difficulties understanding the distribution and spatial characteristics of health outcomes and determinants. We examined the degree to which environmental and landscape factors relate to the spatial and temporal variability in wildlife surveillance data. This poster reports preliminary results from a data quality analysis of wildlife surveillance data from Ontario, Canada for white-nose syndrome in bats. The landscape characteristics that were examined included proximity to roads and major cities, natural areas and parks, seasonality, and topography. Species range and habitat use as well as the disease’s environmental and landscape characteristics were modeled using the same methodology to compare where surveillance areas overlap. Examining the biases in wildlife surveillance sample collection in conjunction with environmental determinants of species distributions and related health outcomes can help gain insights into vulnerable populations that may be under sampled or inaccessible and where surveillance efforts can be improved prior to emergence or re-emergence of disease.